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Undergraduate Computer Science Students: Measuring Perception, Marketing and Satisfaction


Case Study By: Katharine Blanchard

For: Professor Dave Scholz, MA Course: MCM 712 Public Relations Research

DeGroote School of Business McMaster University

June 1, 2011


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Table of Contents

Abstract Page 3

Background and Literature Review Page 4

Research Problem Page 7

Hypothesis Page 8

Methodology Page 8

Steps and Approach Page 9

Subjects Page 12

Potential Issues Page 13

Data Analysis Page 15

Key Findings Page 16

Discussion 2

Limitations Page 24

Future Work Page 25

Appendix 1: Survey Questionnaire Page 27

References Page 33


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Abstract

This case study surveyed Computer Science students in years two through four at

McMaster University in Hamilton, Ontario. We performed a cross-sectional sampling of

students in the Computing and Software department. The goal of the study was to

measure satisfaction of the CS program, based on current CS curriculum and the

student’s perceptions of CS. A paper- based questionnaire was administered to the

students with twenty-four questions that sought information on the student’s perceptions,

information sources, curriculum and personal demographics.

The survey was administered to three sections of CS majors and produced a

sample of 100 respondents. Microsoft Excel was used to analyze the data and calculate

the responses. Key findings include two kinds of correlations: one between satisfaction

and the initial medium through which students were acquainted with the program, and the

second one between satisfaction and choice of future career path.

A correlation was found between students’ degree of accuracy in the perception of

their CS program and the original information source they used to find the program. We

also found that satisfaction with course content is linked with students’ intended career

path. Those who desired to attend graduate school were satisfied with both theory and

practice based curriculum, while students who intended to use their education and work

as a consultant desired more practice-based curriculum.


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Background

In recent decades, Computer Science (CS) has become a fashionable field of

study due to the high employability of its graduates. From software development, to

electrical engineering of computer hardware, to system administration, the job market has

a permanent need for highly skilled individuals. However, after the dot-com bubble burst

in 2001, CS enrollments went down dramatically. While enrollment has since recovered,

it never rose to the original level.

The Computer Research Association (CRA) has tracked enrollments and

graduation rates of CS students for the last 40 years. Their April 2011 report states that

during the dot-com era enrollments swelled. In 2001, the average enrollment in CS

departments in U.S. Universities was 398, and in 2007 it dropped by half. Since then

enrollments now average 253 students per department (Thibodeau, 2011).

Expectations and perceptions of the field continue to evolve with new trends in

technology. As computer technology changes, perceptions of CS change as well. For

example, the Millennials of today who have grown up with an Xbox and Nintendo may

envision a CS program as a carrier in the entertainment industry. The “Nintendo

generation” as Guzdial and Soloway (2002) have labeled the Millennials, are motivated

by what they perceive CS to be. Often times this generation of student seems less

satisfied with the curriculum and are surprised by the amount of mathematics and theory

that is involved in the field. Teachers perceive a level of dissatisfaction from their

students and may conclude that they expect a practice-based curriculum from the CS

program.


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Students seem to hold the misconception that CS programs are all gaming and

hacking, and when they find out that mathematics and theory are involved, this may lead

to the decrease in enrollments and low satisfaction rates. “Students often have

misconceptions about the field of computer science…other students like to play video

games, so their dreams are to become video game programmers. They more often than

not do not realize the mathematics and computer skills necessary for such endeavors”

(Beaubouef & Mason,p.103)

A 2006 study from the University of Washington Computer Science and

Engineering Department reports that after the dot-com collapse of 2001 they were facing

“a collective struggle” to figure out what to teach and how to teach it. They named

several common problems that prompted the study:

• A decline in student satisfaction and enrollment in introductory courses

• A decline in applicants to the major, especially women

• Inconsistency in teaching among instructors

• A lack of basic programming skills reported by upper-division instructors

The study found that after redesigning their introductory CS courses to emphasize

problem solving, procedural decomposition and mastery of basic skills, enrollments

increased, satisfaction rates rose, and more women entered the program (Reges, p.293).

While the curriculum changes were only in place for four semesters at the time of

the study, early results showed a marked increase in satisfaction and higher evaluations.

The study states an obvious challenge that many teachers face: different levels of ability

exist within every class. Designing one curriculum that fits all levels of students seems to

be a universal struggle. Even more so when students enter CS programs with


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misconceptions of what the field involves. Student satisfaction depends in large part on

what they expect from these programs. If students come with an over-glamorized idea of

what CS is- even if the teachers are teaching the right things- the students may perceive

that they are not learning what the program promised.

One longitudinal study performed by the Rochester Institute of Technology

surveyed undergraduate students of CS over a three-year period. The study examined

students’ needs based on several measures including their previous computer

background, knowledge of programming languages, what attracted them to a degree in

CS, demography and finally personal learning style. The research found that students

often had a different idea of what CS studies entailed prior to entering the program and

this among other factors led to a high attrition rate in the program (Howles, p.23).

If a CS department teaches the fundamentals of the field mixed with current

trends, is this in contrast to the students’ desires to only learn current trends in

technology? Often times teachers are left convincing students that the curriculum they

are presenting will be suitable for the job market. Teachers want to impart knowledge

that is important for its own sake rather than have the ever-changing job market dictate

the curriculum. Students desire to learn the latest and the greatest- to be able to compete

in the current marketplace. However, as educators maintain, if students learn

fundamentals they will have the skills to always learn the latest technology on their own.

When faced with declining enrollment, should CS departments cater to a trendier

approach or stick to the fundamentals of the field? CS professors want to teach the

fundamentals of the science while administration desires high enrollment. This may

produce a marketing of CS that is at odds with the curriculum as understood by faculty.


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The solution may be to teach fundamentals and problem solving skills while at the same

time presenting the latest technological trends.

One study from 2007 conducted in the UK, found the same problem only in a

different department. Marketing professors at the University of Edinburgh Business

School performed a series of 23 exploratory interviews, followed by a national survey of

marketing educators. Their purpose was to investigate the educators’ perceptions as to

the primary purpose of undergraduate degrees: theory or practice? The research asked

specifically to what degree curricula should be driven by industry (Tragear, Dobson, &

Brennan, p. 66). They then conducted bivariate tests to identify whether any of the

outcomes were linked to respondents’ profile characteristics. They found that

respondents who were in teaching roles agreed that curricula should pose a sufficient

intellectual challenge (theory-based), whereas all other respondents disagreed with this

statement (Tragear, Dobson, & Brennan, p.73). Interestingly, respondents who worked in

a consultant capacity felt that the curriculum should be industry driven (practice-based).

Our study found similar trends in that we found correlations among the same variables.

Research Problem

The study aimed to answer the following questions:

Q1: How do student expectations affect program satisfaction?

Q2: What types of marketing sources influence students’ perception of CS

program expectations?

Q3: Are students satisfied with the mix of foundational knowledge and practical

application involved in their undergraduate program?


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Hypothesis

The aim of this case study was to measure levels of satisfaction based on students’

perceptions of their CS program. At the outset the researcher had two main hypotheses:

Misinformed perceptions of the University’s CS program were affecting their satisfaction

with the program, and student’s satisfaction is linked to their perception of course content

(theory vs. practice based). We intended to show that perceptions vary according to the

information source used and that satisfaction rates have a direct correlation with career

choice.

Methodology

The study was a cross-sectional single study where paper questionnaires were

distributed to students in the Computing & Software department at McMaster University.

A series of 24 questions were developed to measure student perceptions and satisfaction

of current CS programs. The questionnaire was then reviewed and edited by Prof. Dave

Scholz, research advisor, to ensure that the questions were well crafted. The survey

consisted of twenty-one close-ended questions written to measure student satisfaction,

perception and marketing of the CS programs. The questionnaire also included four open-

ended questions, which allowed for students to further expand on specific aspects of the

program.

The questionnaire was distributed at the beginning of class and students were

informed it was to be filled out on a voluntary basis. The questionnaire was given to

three classes representing two different sections of the department: network engineers

and software design students. The directions for the survey indicated the purpose of the

study and that no names were required as all responses were to be anonymous. The


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students were given as much time as was needed to fill out the questionnaire. The

students took an average of 20 minutes to complete the survey.

The goal of the survey was to gain data from a variety of students in the

department in order to measure varying perceptions and rates of satisfaction according to

their specific field of study within Computing & Software. The questionnaire was

deployed on two different days, March 31 and April 4, 2011. For the purpose of this

questionnaire, the terms “theory”, “foundational” and “mathematical” are used

interchangeably to measure the theoretical side of the field.

Steps and Approach

The first step in the research was to perform a historical review of published

studies involving measurement of CS students’ satisfaction based on program perception.

Obtaining the literature review involved two sessions with librarians at McMaster

University. In the preliminary research session we focused our research on the prominent

subjects areas of the case study: Computer Science and Communications. The search

results included usually one of the two categories, but only one result included both. The

Rochester Institute of Technology 2009 case study then lead the researcher to other

journals in the field of CS that held similar studies. Another extensive search was

performed that included sub-category topics including variations of the following: student

expectations towards program fulfillment, undergraduate studies, satisfaction, marketing,

computing and software, and a variety of many others. One abundant source was the

Association of Computing Machinery’s digital library where several of our resources

were found.


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The second step was to design the survey instrument. The paper-based

questionnaire was designed after receiving input from the graduate advisor for the

Computer Science in the department of Computing and Software. The survey questions

focused on uncovering student’s perceptions and expectations of CS prior to entry in the

program, what factors influenced their program decision, and how satisfied they were

with the University’s CS program. Questions were designed to measure student’s overall

satisfaction as well as specific areas of the curriculum. The data was then evaluated to see

if a correlation lies between overall program satisfaction and individual respondent

characteristics.

Once the questionnaire was completed and approved, the graduate advisor

administered the survey on behalf of this researcher, on two separate dates at the

beginning of three CS classes. Total distribution of the surveys was 101 and exactly 101

were returned. Only one survey was returned blank which gave the researcher a sample

of 100 surveys. Questionnaires were then labeled with a corresponding letter and number

(A1, A2, B1, B2, C1, C2 etc.…) and divided into three groups according to class and date

of distribution. The return rate was based on class size and the breakdown is shown

below:

• Group A: Software Design, administered on March 31, 2011. Responses: 14

• Group B: Software Design, administered on March 31, 2011. Responses: 31

• Group C: Networks, administered on April 4, 2011. Responses: 56

One point should be added regarding our method of recording the results of the

survey. It was important to record the result of each survey, rather than just record the

final results. That is, we could have just recorded the number of different answers to


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each question, but we would have lost valuable information doing it this way. Our

method – having the results of each questionnaire recorded as a row in Excel – allows us

to extract important information from the survey.

For example, besides knowing how many people are interested in a career as a

consultant, we can extract correlations such as “how many people who desire to be

consultants are also satisfied with the program” and obtain percentages of students who

are satisfied with the program according to different career selections. Excel facilitates

such data analysis, and was done by using the “sumif” and “countif” functions. For

example, column D in our Excel sheet contains the answers to question Q2 (“Original

perception of the program” with possible answers “Accurate,” “Somewhat accurate,”

“Not accurate at all,” “d/k,” “n/a”). On the other hand, column E in our Excel sheet

contains the answers to question Q3. It contains, recorded as a “1”, the students who

were influenced by the “McMaster Website” to make their decision to study in their

program (see Table 1).

D E F G H I J K Q3-1 Q3-2 Q3-3 Q3-4 Q3-5 Q3-6 Q3-7

Q2 Results

Brochure Website Campus visit

Counselor Friend Company Word of mouth

Accurate 1 7 4 5 4 1 4 Somewhat accurate

11 20 11 10 12 4 14

Not accurate

3 5 0 1 0 0 2

Table 1 Note. Table 1 contains the conditional sums from the original Excel sheet demonstrating a bivariate analysis of questions 2 and 3. We can now do a bivariate analysis of these two, finding a correlation of how

many students – from among those who were swayed by the McMaster Website – would


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consider their original impression “somewhat accurate.” This was obtained with the

following Excel formula:

=SUMIF(D2:D102,"=2",E2:E102)

By using this formula, Excel adds up all the “1” in column E for which the corresponding

row in column D contains a “2” (which represents “somewhat accurate” answer to Q2).

Thus, we obtain the number of students that were both swayed by the McMaster Website

and would consider their original impression “somewhat accurate.” We do the same for

the other sources of information and all possible original perceptions. We then used the

built-in Excel functions to produce charts that demonstrate our correlations:

Figure A

Subjects

The subjects for the study are second through fourth year students currently

enrolled in McMaster University’s undergraduate Computing & Software program. The

survey was conducted during three undergraduate classes; one class of network engineers

and two classes of software design students. This provided a range of students from


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within the program varying in age, gender, program year and program of study. The

students range from second year students to older professionals who already hold jobs in

the computer industry.

The Department of Computing & Software offers several program majors.

Therefore the classes are generally made up of a combination of students from different

majors: CS, Software Engineering, Mechatronics and Embedded Systems. We were

unaware prior to the survey in which majors the students were enrolled. After the survey

results were calculated, we found that the majority of students 53%, are enrolled in

Computer Science, 34% in Software Engineering, 10% in Mechatronics and 2% selected

“Other” as their major. Figure B displays the program enrollment of the students

surveyed:

Figure B

Potential issues

The first area of research involves undertaking a thorough literature review to find

supporting research. One prior study was found that measured the two main categories,

student satisfaction and undergraduate CS programs. The mixture of two very different

fields of study, CS and Communications proved a challenge for finding research that


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incorporates communications theory into the CS field. The researcher addressed this

issue by meeting with three senior librarians at McMaster University. In depth searches

were performed in several databases that encompassed multiple fields of study to gather

resources that matched the subject matter for this case study. Once an appropriate

resource was found, we then used the bibliography to refine and expand the search. In

the end three relevant studies were found and cited.

Another obstacle to the research project was illegible handwriting. The survey

contained both closed and open-ended questions; the responses had to be interpreted very

carefully in order to not taint the data. Roughly 7 of the 100 questionnaires had

potentially illegible answers. In fact, a certain pattern emerged from the open-ended

questions where responses were of the same theme and therefore easily interpreted. This

is to be expected as students had similar experiences with teachers, curriculum and

overall program attributes, resulting in similar answers to the open-ended questions.

Other potential problems included poor English skills. For many respondents

English is a second language. Also, the study was done at the end of the semester,

therefore due to fatigue, many students may have chosen to not participate or complete

the survey in its entirety. The researcher found that because the respondents were given

ample time to complete the questionnaires there were no problems in understanding the

responses of the students and cases of poor English skills did not surface. As far as

fatigue, or lack of motivation to complete the questionnaires, the researcher only found

one instance where a respondent turned in a blank questionnaire.

Another obstacle arose during the measurement phase. In a few cases where the

question stated for the respondent to circle one answer for each question, the student


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either crossed out or chose more than one response. The researcher gave the best

assessment possible of each question and selected the most obvious response or in some

cases chose a “d/k” (don’t know) response for questions where the response was

impossible to extract.

Data Analysis

As we mentioned, students were comprised of second to fourth year CS students,

within the faculty of Engineering, 53 were enrolled in Computer Science, 34 in Software

Engineering, 10 in Mechatronics, and 2 responded “Other”. Due to our convenient

sample size, each person equals exactly 1% of the data results. Of the 100 respondents,

90% were male and 10% were female. When surveyed about their intended career path,

45% desired to work for a large corporation such as RIM or IBM, 22% preferred a small

start-up company, 16% intended to apply for graduate school, 13% desired to work as a

consultant and 8% planned to use their CS degree to work as system administrator.

Figure C shows preferred career path of the respondents. An outcome higher than 100 is

due to students being able to choose more than one preferred career path.

Figure C


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Key Findings

The three research questions set out to measure perception, marketing, and

satisfaction of CS students. A key finding within the measurement of perception and

marketing answered research questions one and two. A correlation was found by

comparing two different survey results. Survey question one asked about the students’

original perception of the CS program and question two inquired what information source

(brochure, website, word of mouth, etc.) they used in their program choice. The results

are shown in Figure D and demonstrate a correlation between students’ degree of

accuracy in perceptions and the resource they used.

Figure D

Three information sources stood out as the most accurate in communicating the

CS program: campus visit, friend and company reference. The brochure and website

were rated as less accurate sources by the students surveyed, and those who chose the

traditional forms of marketing showed a15-20% inaccuracy rates, with word of mouth

and high school counselor coming in third and fourth place.


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A second key finding extracted from the above data also supported research

question two. The highest accuracy results came from information sources that

presumably involved speaking with a person. This shows the importance of a personal

connection when marketing a program and the students’ perception of the program.

While websites and brochures are necessary resources for any institution they do not take

the place of an on-site visit or personal reference.

Research question three was answered with a third key finding in the area of the

satisfaction rates based on the curriculum. In order to compare students that were “very”,

“somewhat” or “not at all” satisfied with their programs, the researcher compared two

questions on the survey measuring career path and overall satisfaction. A correlation was

found between the students’ future plans and how satisfied they were with the program.

Question 18 asked what their preferred career path was and question 17 asked them to

rate how satisfied they were with their program. We then drew a correlation between

those who were “very” or “somewhat” satisfied and their preferred career path and those

who were “not at all” satisfied. Figure E demonstrates these findings:

Figure E


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This revealed an interesting finding in that the highest level of program

satisfaction came from those students who intended to go to graduate school, while the

lowest program satisfaction came from those who desired to become a consultant. This

makes sense when looking at the curriculum challenge of theory vs. practice. What we

found is that satisfaction with course content is linked with students’ intended career

path. Those who desired to attend graduate school were satisfied with both theory and

practice-based curriculum, where students who intended to use their education and work

as a consultant desired more practice-based curriculum. Results also showed students

motivations were not only to get a job, but many sought growth and understanding as

their reason for choosing a CS program. For them the value of the degree was more than

social status, but rather a personal accomplishment.

Research question three was further answered with a finding in the area of the

satisfaction rates of theory vs. practice-based curriculum. The curriculum challenge of

how much theory and how much practical application a teacher should impart depends on

many factors. Foundational knowledge is important in any field and especially in a tech-

based field like CS. However, often students desire to “skip” to the end and learn the

practical application instead of the theory involved. For example, many of the students

expressed this in their open-ended responses when asked what they would change in their

program:

• “More relevant information and less math theory”

• “Add a practical course in year 2 to inspire students so we know what the

mechatronics field is actually like”


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• “I would like to see more practical labs; specifically in robotics and

mechatronics”

• “ More emphasis on the practical application of theory we learn”

The survey also measured the two sides of the theory vs. practice spectrum. Two

questions measured the students’ perception of the theoretical content by asking them

how relevant they felt mathematics and foundational knowledge would be to their

intended profession. 53% agreed or strongly agreed that mathematics was relevant to

their intended profession, while 21% disagreed or strongly disagreed. 62% stated that the

foundational knowledge they learned was “very valuable” or “somewhat valuable” to

their future careers, while 33% believed it to be only “partially” or “barely” valuable.

Figures F and G demonstrate these results:

Figure F


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Figure G

We then measured two common skills that CS students acquire in any CS

program: coding and programming languages. We asked if they were satisfied with the

amount of the coding and programming languages their CS program taught. In this area

of the curriculum, only 41% of students were satisfied with the amount of coding they

had learned, and 45% were unsatisfied. When asked if they were satisfied with the

amount of programming languages they had been exposed to 43% were satisfied, while

40% were not satisfied. These results seem to demonstrate that students are harder to

please on the practical application side of curricula. Figures H and I demonstrate these

results:


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Figure H

Figure I

Interestingly, when asked directly “Do you agree that the best approach to an

undergraduate degree in your field is a mixture of theory and practice?” A majority of

students at 87% agreed or strongly agreed that a mixture was the best approach while

only 5% disagreed. Figure J demonstrates these results:


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Figure J

One promising result was that 78% of the students agreed or strongly agreed that

the CS program had taught them problem solving skills. Only 10% disagreed or strongly

disagreed with the statement. This shows a definite strength in the CS program’s

curriculum, as it is impossible to teach students everything they need to know, but by

teaching them problem-solving skills, they will be able to tackle any challenge that may

arise. In a sense, theory or practice based curriculum does not matter, as long students

are taught how to solve problems they will be successful with new technologies.

Discussion

As we stated in the hypotheses, we believed that students’ satisfaction was linked

to their perception of course content (theory vs. practice based) and we intended to show

that perceptions vary according to the information source used. The research also aimed

to prove that satisfaction rates have a direct correlation with career choice. As CS

programs across the globe aim to keep enrollments high a decade after the dot-com burst,

our research findings reveals practical steps departments could take to increase their


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enrollment and overall satisfaction rates. For example, the influence on student’s

perception of the CS program by way of a personal contact (campus visit, friend or

company reference) shows the importance and power of how the CS program is

communicated to students prior to their entrance. 59% of the students said the

information source they were exposed to was only somewhat accurate in its portrayal of

the program. While 24% said it was accurate and 11% said it was not, 6% did not know

or the question was not applicable. The results show that based solely on the key

marketing materials used by the university, brochure, website and campus visits, 83% of

the students developed an accurate or somewhat accurate perception of the program.

A personal reference from a company or friend and a campus visit, influences

their perception and program choice more than any marketing material. The University

would do well in focusing future communication of its programs by building an advocacy

program in which alumni and current students become the primary “advertisement” for

the CS program. A visit to local high schools and businesses where a personal testimony

of their program experience is shared would be very beneficial according to our study.

In the area of curriculum, the research sought to find what mix of theoretical and

practical material influenced student satisfaction. It is interesting to note that for many

professors, theoretical and foundational tend to be the same thing. Whereas students

seem to equate theoretical with “not practical”, and foundational with the basics of the

field i.e.: that which can be used in practice. For example the results from questions 12

and seven sought to measure students’ perceptions of the theoretical material by using

two different words to describe the same thing. The results from question seven

demonstrate that 63% of students view the CS program as too theoretical. However, the


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results from question 12 show that 62% of the students felt that “foundational”

knowledge was valuable or very valuable to their intended profession. This shows a

disparity between an understanding of the curriculum and what is foundational

knowledge. While students feel that foundational knowledge is valuable, at the same

time they feel their program is “too theoretical”. On page nine we stated that we assumed

the words “theoretical”, “foundational”. “mathematical” denote the same thing. But in

the study we discovered that they have a slightly different connotation to the students.

The researcher gathers from this result that professors consider using the word

“foundational” in lieu of “theoretical” whenever possible with students. This implies an

almost practical approach and perhaps a better way of communicating to the students an

important part of their studies.

Limitations

One limitation of the study was that only a select section of CS students were

surveyed. Most CS programs are made up of several majors such as electrical engineers,

mechatronics and game design. This survey was administered to respondents that were

53% Computer Science majors, 34% Software Engineers and 10% Mechatronics. The

unintended exclusion of other majors within the CS field is a limitation of the study.

A second limitation was that the questionnaires were administered at the end of

year. Student perceptions and ability to recall information required by the questionnaire

may have been less accurate compared with conducting the survey at the beginning of the

semester.


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A third limitation is that the researcher was introduced to the research subject by

the graduate advisor who is also her husband. Every effort to avoid personal bias was

taken; it is possible that due to her personal connection with the department supervisor,

this may have influenced the results.

Other limitations exist in the area of the sample population. The sample was not

completely random as the respondents were students from two particular courses taught

by the same professor. Also students came from a variety of program years and therefore

levels of maturity varied accordingly.

Future Work

The research in this paper can be further extended. It seems that little research

has been done in the application of Communication to the teaching of CS and marketing

of programs to students.

It would be interesting to see if new patterns would emerge if this questionnaire

were to be applied over the period of several years. However, it would be difficult to

preserve the same conditions as the sample group would not be the same, and professors

would change over the years as well.

Another angle of study would be to survey the CS professors and get their

perspective, especially in the “theory vs. practice” dichotomy. In particular, from a

Communications point of view, it would be advantageous for professors to choose their

language well, and to research how to improve their pedagogical skills by improving their

communication with the students.


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A study that involved a more thorough review of the marketing procedures of the

CS department could obtain further insight into the correlation we found with satisfaction

based on information resources.

Future work could also include measurement in the pre and post phase of the

areas involved. For example, an “exit” or graduation survey as a follow up to the

questionnaire would provide satisfaction results from those who have completed the CS

program instead of those who are currently enrolled. Along that same vein, mailing a

satisfaction survey to alumni five years after graduation would be an interesting follow

up to the correlation found in the research between satisfaction and intended career path.

In the pre-phase analysis, research could be done at the entry level to measure

how well prepared the students are coming into the program, and compare it to their

program satisfaction. This would explore a correlation between student satisfaction and

prior content knowledge.

We should also note that the researcher contacted the author from the “Back to

Basics” study done at the University of Washington to see if there were any updated

results from his curriculum changes in 2006. He has yet to reply. We hope to update our

study with his results at a later time.


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Appendix 1: Survey Questionnaire

Questionnaire 1 Students in department of Computing & Software Thank you for completing this survey. The results will be used to improve your program and as part of a graduate research project assessing program fulfillment. Please note that your responses are confidential. Do not put your name or student number on the questionnaire. Please circle the answer that best represents you. 1) Which program are you in: (circle one)

a. Computer Science b. Software Engineering c. Mechatronics Engineering d. Other: __________________________________________________(please fill in your

program) 2) Recall how your program was initially advertised to you, and the perceptions you formed. Today, would you say that your original perception of the program was: (Circle One)

1=Accurate 2=somewhat accurate 3=Not accurate at all d/k=Don’t know n/a=Not applicable

3) Recall what sources of information (if any) influenced your decision to study in your program. (Select all that apply)

o Brochure o McMaster University Website o Campus Visit o High School Counselor o Friend o Company o Word of mouth o Other: _________________________ o d/k=don’t know o n/a=not applicable


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4) Do you consider the perception your formed from the above sources of information to be an accurate portrayal of the program, as you know it now?

1=Yes 2=Somewhat 3=No d/k =Don’t know n/a=Not applicable

5) What was your reason for entering the program: (Circle one or more)

1= To further my understanding of computational sciences 2= To get a degree 3= To get a job 4= Family expectations 5= Social Status Other: ___________________________________ (please specify)

d/k=Don’t know n/a=Not applicable 6) Have your goals changed as a result of your program? (Circle One)

1=Yes 2=Somewhat 3=No d/k =Don’t know n/a=Not applicable 6b) If you stated that your goals have changed or have somewhat changed from when you entered the program, please identify what your goals are now:

7) Would you consider the program mostly practical, mostly theoretical or a good mixture of both? (Circle One)

1= Practical 2= Theoretical

3=A good mixture of both d/k= Don’t know n/a= Not applicable


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8) What mix of foundational and technical skills were you expecting to receive? (Circle One)

1=Only foundational knowledge 2= Mostly foundational, slightly technical 3=Both equally 4=Slightly foundational, mostly technical 5= Only technical skills

d/k= Don’t know n/a= Not applicable

For the following statements, please state how much you agree with each of them. 9) I am satisfied with the amount of coding I have done in this program. (Circle One) 1=Strongly Agree 2=Agree 3=Undecided 4=Disagree 5=Strongly Disagree d/k= Don’t know n/a=Not applicable 10) I am satisfied with the number of programming languages I have been exposed to in the program. (Circle One) 1=Strongly Agree 2=Agree 3=Undecided 4=Disagree 5=Strongly Disagree d/k= Don’t know n/a=Not applicable 11) I feel the mathematics I have learned is relevant to my field. (Circle One) 1=Strongly Agree 2=Agree 3=Undecided 4=Disagree 5=Strongly Disagree


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d/k= Don’t know n/a=Not applicable 12) How valuable is foundational knowledge to your intended profession? Please choose between one and five where one means it is very valuable and five means it is not valuable at all. (Circle One) 1-Very valuable 2- 3- 4- 5-Not valuable at all d/k= Don’t know n/a=Not applicable 13) As of today, has your program met your expectations? (Circle One)

1=Yes and has exceeded my expectations 2= Yes it has met my expectation 3=It has met some of my expectations but not all 4=Not at all

d/k= Don’t know n/a=Not applicable 14) Are you satisfied with your choice of program? (Circle One)

1= Yes 2=Somewhat 3=No d/k =Don’t know n/a=Not applicable

15) As of today, would you agree that you have learned problem-solving skills in your program? (Circle One) 1=Strongly Agree 2=Agree 3=Undecided 4=Disagree 5=Strongly Disagree


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d/k= Don’t know n/a=Not applicable 16) Do you agree that the best approach to an undergraduate degree in your field is a mixture of theory and practice? (Circle One) 1=Strongly Agree 2=Agree 3=Undecided 4=Disagree 5=Strongly Disagree d/k= Don’t know n/a=Not applicable 17) Overall how satisfied are you with your program? (Circle One)

1=very satisfied 2=somewhat satisfied 3=not satisfied at all d/k =Don’t know n/a=Not applicable

18) What is your preferred career path?

o Small start up company o Large corporation (ex: RIM, Google, IBM) o System Administrator o Consultant o Graduate School o Other: _________________________________(Please Specify)

d/k =Don’t know n/a=Not applicable

19) My gender is: (Circle One)

• Male • Female

20) What month and year did you begin your studies at McMaster? MM YYYY


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d/k =Don’t know n/a=Not applicable

21) What year of your program are you in? (Circle One):

o Year 1 o Year 2 o Year 3 o Year 4 o Year 5 o d/k =Don’t know o n/a=Not applicable

22) What would you change about your program? 23) What would you add to your program? 23) What would you leave the same about your program? Any Additional Comments you would like to add? Thank you for completing the survey.


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References

Beaubouef, T. and Mason, J. (2005). Why the high attrition rate for computer science students: Some thoughts and observations. Special Interest Group Computer Science Education, 37(2),103-106. Guzdial, M. and Soloway, E. (2002) Teaching the Nintendo generation to program. Communications of the ACM, 45(4), 17-21. Hagan, D. and Markham, S. (2000). Does it help to have some programming experience before beginning a computing degree program? Association of Computing Machinery, 25-28. Howles, T. (2007). Preliminary results of a longitudinal study of CS trends, behaviors and preferences. Consortium for Computing Sciences in Colleges, 18-27. Lewis, C. (2007). Attitudes and beliefs about computer science among students and faculty. Special Interest Group Computer Science Education, 39(2), 37-41. National Research Center Inc., Excel for data analysis. (2003). Retrieved from http://www.n-r-c.com/excelhandbook.pdf Rashid, R. (2008). Inspiring a new generation of computer scientists. Communications of the ACM, 51(7),33-34. Reges, S.(2006). Back to basics in CS1 and CS2. Association of Computing Machinery, 293-267. Thibodeau, P. Computerworld April 12, 2011. Retrieved from http://www.computerworld.com/ Tregear, A., Dobson, S.,Brennan, M. and Kuznesof, S. (2010). Critically Divided? How marketing educators perceive undergraduate programmes in the UK. European Journal of Marketing, 44 (1) 66-86.

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